Today, I’m going to walk you through this R Markdown document to demonstrate its anatomy and functionality, without narrating. Sit back, relax, and take note of the overall process. We’ll discuss subsequently.

Reading in the data

#this is a built-in dataset. See details here:
?swiss

swiss <- swiss

This dataframe contains “standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.”

Let’s look at the data

#First, I'll load my packages.
library(tidyverse)
library(skimr)

# As you see, when I run that code, I receive some messages about those packages, and I don't really feel like seeing those, so I'll change the chunk options to control the output.

#Next, I'll skim the dataframe

skim(swiss)
Data summary
Name swiss
Number of rows 47
Number of columns 6
_______________________
Column type frequency:
numeric 6
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Fertility 0 1 70.14 12.49 35.00 64.70 70.40 78.45 92.5 ▂▂▇▇▅
Agriculture 0 1 50.66 22.71 1.20 35.90 54.10 67.65 89.7 ▃▃▆▇▅
Examination 0 1 16.49 7.98 3.00 12.00 16.00 22.00 37.0 ▅▇▆▂▂
Education 0 1 10.98 9.62 1.00 6.00 8.00 12.00 53.0 ▇▃▁▁▁
Catholic 0 1 41.14 41.70 2.15 5.20 15.14 93.12 100.0 ▇▁▁▁▅
Infant.Mortality 0 1 19.94 2.91 10.80 18.15 20.00 21.70 26.6 ▁▂▇▆▂

Let’s visualize something

swiss %>% 
  #remember how those town names weren't really a column? let's fix that right quick.
  rownames_to_column(var = "Town") %>% 
  #now to the viz
  ggplot(aes(x = Fertility, y = Infant.Mortality, color = Town)) +
  geom_point() +
  coord_flip() +
  theme(legend.position = "none")

Let’s make something interactive

You can hover over the points to see the town name and estimates of fertility and infant mortality.

library(plotly)

#I'm going to save the above visualization as an object
swiss_viz <- swiss %>% 
  rownames_to_column(var = "Town") %>% 
  ggplot(aes(x = Fertility, y = Infant.Mortality, color = Town)) +
  geom_point() +
  coord_flip() +
  theme(legend.position = "none")

ggplotly(swiss_viz)

Conclusion

The mean standardized fertility measure among these towns in 1888 was 70.14. I really like what I’ve made, and I think it’s worth rendering so I can share it with my collaborators.